Original Data for Sentiment of Smoking Cessation Discussions on Social Media in the Context of EVALI: A Hybrid Machine-Learning-Based Content Analysis Approach, by Yuqi Zhang and Yingning Wang

Paper Abstract:Objective:This study explores the public discourse on Twitter regarding smoking cessation before, during, and after the EVALI outbreak, aiming to discern any changes in sentiment, topics, and content and help with contemporary smoking cessation efforts.Methods:Using snscrape, English tweets from September 1, 2018, to January 31, 2020, were collected and filtered for smoking-cessation-related keywords. Sentiments were evaluated with VADER, classifying tweets into positive, negative, or neutral. Topics were identified through Latent Semantic Analysis, and LexRank extracted representative sentences for content analysis.Results:There was a significant increase in smoking cessation discussions in September 2019, coupled with a decline in average sentiment score. The "Vaping" theme dominated, characterized by a drop in sentiment. Opinions on vaping were divided; some promoted e-cigarettes as a tool for smoking cessation while others expressed negativity, largely criticizing regulations on non-tobacco-flavored e-cigarette products.Conclusions:These findings highlight the urgency for policymakers to intervene on social media, aiming to amplify targeted smoking cessation content and improve the long-lasting less effective communication around e-cigarette policies, ensuring that public discourse is informed and constructive.